Ragesh Jaiswal

collaborators

7 papers

cs.DS2026

Improved Algorithms for Clustering with Noisy Distance Oracles

Pinki Pradhan, Anup Bhattacharya, Ragesh Jaiswal

Bateni et al. has recently introduced the weak-strong distance oracle model to study clustering problems in settings with limited distance information. Given query access to the st…

cs.DS2026

Fast $k$ -means Seeding Under The Manifold Hypothesis

Poojan Shah, Shashwat Agrawal, Ragesh Jaiswal

We study beyond worst case analysis for the $k$ -means problem where the goal is to model typical instances of $k$ -means arising in practice. Existing theoretical approaches provide…

quant-ph2025

A Quantum Approximation Scheme for k-Means

Ragesh Jaiswal

We give a quantum approximation scheme (i.e., $(1 + ε)$ -approximation for every $ε > 0$ ) for the classical $k$ -means clustering problem in the QRAM model with a…

quant-ph2025

Quantum (Inspired) $D^{2}$ -sampling with Applications

Poojan Shah, Ragesh Jaiswal

$D^{2}$ -sampling is a fundamental component of sampling-based clustering algorithms such as $k$ -means++. Given a dataset $V \subset R^{d}$ with $N$ points and a center set $C…

cs.DS2025

Robust-Sorting and Applications to Ulam-Median

Ragesh Jaiswal, Amit Kumar, Jatin Yadav

Sorting is one of the most basic primitives in many algorithms and data analysis tasks. Comparison-based sorting algorithms, like quick-sort and merge-sort, are known to be optimal…

cs.DS2025

Clustering What Matters in Constrained Settings

Ragesh Jaiswal, Amit Kumar

Constrained clustering problems generalize classical clustering formulations, e.g., $k$ -median, $k$ -means, by imposing additional constraints on the feasibility of clustering. Ther…